Job Description

Overview:

Focus: Model Optimization, Fine-Tuning & Strategic AI

Experience: 8+ Years / Expert

Role Summary:

You represent the pinnacle of Applied AI engineering. You are not just using APIs; you are optimizing the models themselves. You understand the mathematics behind the attention mechanism, you know how to squeeze performance out of GPUs, and you can customize models for specific domains. You provide the high-level technical vision and handle the most difficult edge cases. . You define technical standards, lead high-impact AI initiatives, and advise C-level leaders.


Responsibilities:

Key Responsibilities:

Model Fine-Tuning: Implement PEFT (Parameter-Efficient Fine-Tuning), LoRA, and QLoRA to adapt open-source models (Llama 3, Mistral) to specific client domains. Optimization & Quantization: Perform model quantization to reduce inference costs and latency without sacrificing quality. Manage Dense Vectors and embedding optimizations. State-of-the-Art Exploration: Continuously research and implement the latest advancements (e.g., State Space Models, Long-Context optimizations) into client deliverables. Strategic Consulting: Act as a trusted advisor to C-level client executives, defining the "Art of the Possible" and guiding long-term AI roadmaps. Thought Leadership: Present thought leadership in industry forums.

Requirements:

Technical Requirements:

Deep Learning: PyTorch/TensorFlow, Transformers architecture internals, Attention mechanisms. Model Ops: Serving custom models (vLLM, TGI), GPU memory management, Quantization techniques (GGUF, AWQ). Advanced Data: Training data curation, synthetic data generation, RLHF concepts. Tech Leadership: Ability to define the technical culture and set standards for the entire FDE organization.

Soft Skills:

Executive communication and ability to influence C?level leaders. Thought leadership and industry presence (conferences, playbooks, forums). Cross?org leadership and conflict resolution. Ability to define long?term AI vision and cultural standards.

Strategic decision?making balancing cost, risk, and performance.